Functions approximation based on locally learning techniques

This paper presents a new algorithm for approximating a nonlinear function by means of local models. It is proposed a memory-based technique for selecting the best model configuration by comparing different alternatives. A recursive technique for local model identification and validation is presented, together with an enhanced statistical method for model selection. The shapes and locations of receptive fields are changed in an adaptive manner. The learning capabilities are demonstrated by means of some examples.